Thank you very much for downloading using mpi portable parallel programming with the message passing interface. As you may know, people have search hundreds times for their chosen novels like this using mpi portable parallel programming with the message passing interface, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their laptop.

Using Mpi Portable Parallel Programming With The Message Passing Interface

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Big Data for Health

Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.

https://orbit.dtu.dk/files/117885360/PHOTO_D_15_00278R1B.pdf

Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of ~100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

/pdf/crowdsourcing-citizen-science-or-volunteered-geographic-4qhdzgct9c.pdf

Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information

Concurrence of big data analytics and healthcare: A systematic review

The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called “big data” challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data. The MapReduce programming framework uses two tasks common in functional programming: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation. In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields.

/pdf/applications-of-the-mapreduce-programming-framework-to-4lm0m4mle7.pdf

Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends.

The goal of data analytics is to delineate hidden patterns and use them to support informed decisions in a variety of situations. Credit card fraud is escalating significantly with the advancement of the modernized technology and become an easy target for fraudulent. Credit card fraud is a severe problem in the financial service and costs billions of a dollar every year. The design of fraud detection algorithm is a challenging task with the lack of real-world transaction dataset because of confidentiality and the highly imbalanced publicly available datasets. In this paper, we apply different supervised machine learning algorithms to detect credit card fraudulent transaction using a real-world dataset. Furthermore, we employ these algorithms to implement a super classifier using ensemble learning methods. We identify the most important variables that may lead to higher accuracy in credit card fraudulent transaction detection. Additionally, we compare and discuss the performance of various supervised machine learning algorithms exist in literature against the super classifier that we implemented in this paper.

Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study

Chronic lymphocytic leukemia (CLL) is the most common type of blood cancer in Canadian adults. CLL cells are abnormal lymphocytes, which tend to be slightly larger than normal resting lymphocytes and have a condensed appearance to their chromatin. There is a low number of related works on this disease. This paper presents a method to segment normal and CLL lymphocytes into two parts: nucleus, and cytoplasm using a watershed algorithm and optimal thresholding. The goal of this work is reducing the over and under segmentation error of the watershed algorithm by suppressing 1% of the local minima. We tested 140 microscopic lymphocyte images (normal and CLL), and the algorithm obtained 99.92% maximum accuracy for nucleus segmentation, and 99.85% maximum accuracy for cell segmentation. The cytoplasm can be extracted with a 99.63% maximum accuracy with simple mask subtraction. The code for the presented algorithm is shared on the MATLAB® file exchange website.

Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding

This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models.

/pdf/an-ensemble-learning-approach-to-digital-corona-virus-2ks4n3eaxs.pdf

Emad A. Mohammed

Papers

Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends.

Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study

Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding

An ensemble learning approach to digital corona virus preliminary screening from cough sounds.

Peripheral blood smear image analysis: A comprehensive review